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Showing papers by "Jake Vanderplas published in 2014"


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TL;DR: The differences between frequentism and Bayesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters as discussed by the authors.
Abstract: This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python statistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo.

28 citations


Proceedings ArticleDOI
01 Nov 2014
TL;DR: This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python.
Abstract: This paper presents a brief, semi-technical comparison of the es- sential features of the frequentist and Bayesian approaches to statistical infer- ence, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing defini- tions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python sta- tistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo. 1

13 citations